{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import spearmanr\n",
    "from pathlib import Path\n",
    "from scipy.stats import t, norm\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "def flip_zscore(zscore, coeqtlallele, altaf, altallele):\n",
    "    if not pd.isnull(zscore):\n",
    "        if coeqtlallele == altallele:\n",
    "            coeqtlaf = altaf\n",
    "        else:\n",
    "            coeqtlaf = 1 - altaf\n",
    "        if coeqtlaf > 0.5:\n",
    "            return -zscore\n",
    "        else:\n",
    "            return zscore\n",
    "    else:\n",
    "        return np.nan\n",
    "    \n",
    "def flip_allele(altaf, altallele, refallele):\n",
    "    if altaf > 0.5:\n",
    "        return refallele\n",
    "    else:\n",
    "        return altallele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeqtl_withbios_prefix = Path(\n",
    "    \"./coeqtl_mapping/output\"\n",
    ")\n",
    "filter_type = 'filtered_results'\n",
    "\n",
    "def flip_direction(allele1, allele2, zscore2):\n",
    "    if allele1 == allele2:\n",
    "        return zscore2\n",
    "    else:\n",
    "        return -1*zscore2\n",
    "\n",
    "\n",
    "def get_z_score(t_statistic, num):\n",
    "    prob = t.cdf(t_statistic, num - 2)\n",
    "    z_score = norm.ppf(prob)\n",
    "    return z_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_dict = {'CD4T': '#2E9D33',\n",
    "              'CD8T': 'darkgreen',\n",
    "              'monocyte': '#EDBA1B',\n",
    "              'NK': '#E64B50',\n",
    "              'DC': '#965EC8',\n",
    "              'B': '#009DDB',\n",
    "              'cMono': 'peru',\n",
    "              'ncMono': 'y',\n",
    "              'CD4T_individual_100': '#2E9D33',\n",
    "              'CD4T_individual_50': '#2E9D33',\n",
    "              'CD4T_50': '#2E9D33',\n",
    "              'CD4T_150': '#2E9D33',\n",
    "              'CD4T_250': '#2E9D33'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir = Path(\"./coeqtl_mapping/\")\n",
    "bios_replication_filtered_df = pd.read_csv(\n",
    "    workdir/'bios/onlyRNAAlignMetrics_rmLLD/filtered_results/replication_summary.csv', \n",
    "    index_col=0\n",
    ").set_index('celltype')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "celltype = 'CD4T'\n",
    "eqtldf = pd.read_csv(\n",
    "    workdir/f'input/snp_selection/eqtl/UT_{celltype}_eQTLProbesFDR0.05-ProbeLevel_withAF.tsv',\n",
    "    sep='\\t'\n",
    "    )\n",
    "eqtldf['snp_eqtlgene'] = ['_'.join(item) for item in eqtldf[['SNPName', 'genename']].values]\n",
    "eqtl_allele_af_df = eqtldf.drop_duplicates(subset=['snp_eqtlgene', 'AlleleAssessed', 'AF'])\n",
    "eqtl_allele_af_dict = eqtl_allele_af_df.set_index('snp_eqtlgene')[['AlleleAssessed', 'AF', 'alt_allele', 'ref_allele']].T.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "biostype = 'onlyRNAAlignMetrics_rmLLD'\n",
    "celltype = 'CD4T'\n",
    "filter_type = 'filtered_results'\n",
    "\n",
    "coeqtl_df = pd.read_csv(\n",
    "    coeqtl_withbios_prefix/filter_type/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbios{biostype}.tsv.gz',\n",
    "    compression='gzip', \n",
    "    index_col=0, \n",
    "    sep='\\t')\n",
    "coeqtl_df = coeqtl_df.dropna(subset=['t_bios'])\n",
    "coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
    "                            coeqtl_df[['t_bios', \n",
    "                                       'num_individuals_bios']].values]\n",
    "coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
    "                                  coeqtl_df[['SNPEffectAllele', \n",
    "                                             'assessed_allele_bios',\n",
    "                                             'zscore_bios']].values]\n",
    "\n",
    "isConcordant = lambda x:True if x[0]*x[1] > 0 else False\n",
    "coeqtl_df['is_concordant'] = [isConcordant(item) for item in \n",
    "                              coeqtl_df[['MetaPZ', 'flipped_zscore_bios']].values]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>snp_genepair</th>\n",
       "      <th>Gene</th>\n",
       "      <th>GeneChr</th>\n",
       "      <th>GenePos</th>\n",
       "      <th>GeneStrand</th>\n",
       "      <th>GeneSymbol</th>\n",
       "      <th>SNP</th>\n",
       "      <th>SNPChr</th>\n",
       "      <th>SNPPos</th>\n",
       "      <th>SNPAlleles</th>\n",
       "      <th>...</th>\n",
       "      <th>gene1_bios</th>\n",
       "      <th>gene2_bios</th>\n",
       "      <th>assessed_allele_bios</th>\n",
       "      <th>num_individuals_bios</th>\n",
       "      <th>isinteractionterm_bios</th>\n",
       "      <th>snp_genepair_bios</th>\n",
       "      <th>corrected_p_bios</th>\n",
       "      <th>zscore_bios</th>\n",
       "      <th>flipped_zscore_bios</th>\n",
       "      <th>is_concordant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snp_gene1_gene2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rs7605824_SH3YL1_NPM1</th>\n",
       "      <td>rs7605824_NPM1;SH3YL1</td>\n",
       "      <td>NPM1;SH3YL1</td>\n",
       "      <td>2</td>\n",
       "      <td>217730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NPM1;SH3YL1</td>\n",
       "      <td>rs7605824</td>\n",
       "      <td>2</td>\n",
       "      <td>280819</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SH3YL1</td>\n",
       "      <td>NPM1</td>\n",
       "      <td>A</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs7605824_NPM1;SH3YL1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.617874</td>\n",
       "      <td>-3.617874</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs7605824_SH3YL1_CD48</th>\n",
       "      <td>rs7605824_CD48;SH3YL1</td>\n",
       "      <td>CD48;SH3YL1</td>\n",
       "      <td>2</td>\n",
       "      <td>217730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CD48;SH3YL1</td>\n",
       "      <td>rs7605824</td>\n",
       "      <td>2</td>\n",
       "      <td>280819</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SH3YL1</td>\n",
       "      <td>CD48</td>\n",
       "      <td>A</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs7605824_CD48;SH3YL1</td>\n",
       "      <td>0.784422</td>\n",
       "      <td>-0.446946</td>\n",
       "      <td>-0.446946</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs7605824_SH3YL1_RPS13</th>\n",
       "      <td>rs7605824_RPS13;SH3YL1</td>\n",
       "      <td>RPS13;SH3YL1</td>\n",
       "      <td>2</td>\n",
       "      <td>217730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPS13;SH3YL1</td>\n",
       "      <td>rs7605824</td>\n",
       "      <td>2</td>\n",
       "      <td>280819</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SH3YL1</td>\n",
       "      <td>RPS13</td>\n",
       "      <td>A</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs7605824_RPS13;SH3YL1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.489377</td>\n",
       "      <td>-3.489377</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs7605824_SH3YL1_RPL31</th>\n",
       "      <td>rs7605824_RPL31;SH3YL1</td>\n",
       "      <td>RPL31;SH3YL1</td>\n",
       "      <td>2</td>\n",
       "      <td>217730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPL31;SH3YL1</td>\n",
       "      <td>rs7605824</td>\n",
       "      <td>2</td>\n",
       "      <td>280819</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SH3YL1</td>\n",
       "      <td>RPL31</td>\n",
       "      <td>A</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs7605824_RPL31;SH3YL1</td>\n",
       "      <td>0.349601</td>\n",
       "      <td>-1.325633</td>\n",
       "      <td>-1.325633</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs7605824_SH3YL1_RPL3</th>\n",
       "      <td>rs7605824_RPL3;SH3YL1</td>\n",
       "      <td>RPL3;SH3YL1</td>\n",
       "      <td>2</td>\n",
       "      <td>217730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPL3;SH3YL1</td>\n",
       "      <td>rs7605824</td>\n",
       "      <td>2</td>\n",
       "      <td>280819</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SH3YL1</td>\n",
       "      <td>RPL3</td>\n",
       "      <td>A</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs7605824_RPL3;SH3YL1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.854851</td>\n",
       "      <td>-3.854851</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs4147638_SMDT1_ACTB</th>\n",
       "      <td>rs4147638_ACTB;SMDT1</td>\n",
       "      <td>ACTB;SMDT1</td>\n",
       "      <td>22</td>\n",
       "      <td>42475695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACTB;SMDT1</td>\n",
       "      <td>rs4147638</td>\n",
       "      <td>22</td>\n",
       "      <td>42487900</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SMDT1</td>\n",
       "      <td>ACTB</td>\n",
       "      <td>G</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs4147638_ACTB;SMDT1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.748326</td>\n",
       "      <td>3.748326</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs4147638_SMDT1_RPS25</th>\n",
       "      <td>rs4147638_RPS25;SMDT1</td>\n",
       "      <td>RPS25;SMDT1</td>\n",
       "      <td>22</td>\n",
       "      <td>42475695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPS25;SMDT1</td>\n",
       "      <td>rs4147638</td>\n",
       "      <td>22</td>\n",
       "      <td>42487900</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SMDT1</td>\n",
       "      <td>RPS25</td>\n",
       "      <td>G</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs4147638_RPS25;SMDT1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.773036</td>\n",
       "      <td>-5.773036</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs4147638_SMDT1_RPS3A</th>\n",
       "      <td>rs4147638_RPS3A;SMDT1</td>\n",
       "      <td>RPS3A;SMDT1</td>\n",
       "      <td>22</td>\n",
       "      <td>42475695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPS3A;SMDT1</td>\n",
       "      <td>rs4147638</td>\n",
       "      <td>22</td>\n",
       "      <td>42487900</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SMDT1</td>\n",
       "      <td>RPS3A</td>\n",
       "      <td>G</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs4147638_RPS3A;SMDT1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.434777</td>\n",
       "      <td>-4.434777</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs4147638_SMDT1_RPS18</th>\n",
       "      <td>rs4147638_RPS18;SMDT1</td>\n",
       "      <td>RPS18;SMDT1</td>\n",
       "      <td>22</td>\n",
       "      <td>42475695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPS18;SMDT1</td>\n",
       "      <td>rs4147638</td>\n",
       "      <td>22</td>\n",
       "      <td>42487900</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SMDT1</td>\n",
       "      <td>RPS18</td>\n",
       "      <td>G</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs4147638_RPS18;SMDT1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.128733</td>\n",
       "      <td>-7.128733</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rs4147638_SMDT1_RPL11</th>\n",
       "      <td>rs4147638_RPL11;SMDT1</td>\n",
       "      <td>RPL11;SMDT1</td>\n",
       "      <td>22</td>\n",
       "      <td>42475695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPL11;SMDT1</td>\n",
       "      <td>rs4147638</td>\n",
       "      <td>22</td>\n",
       "      <td>42487900</td>\n",
       "      <td>G/A</td>\n",
       "      <td>...</td>\n",
       "      <td>SMDT1</td>\n",
       "      <td>RPL11</td>\n",
       "      <td>G</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>True</td>\n",
       "      <td>rs4147638_RPL11;SMDT1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.896748</td>\n",
       "      <td>-5.896748</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>497 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  snp_genepair          Gene  GeneChr  \\\n",
       "snp_gene1_gene2                                                         \n",
       "rs7605824_SH3YL1_NPM1    rs7605824_NPM1;SH3YL1   NPM1;SH3YL1        2   \n",
       "rs7605824_SH3YL1_CD48    rs7605824_CD48;SH3YL1   CD48;SH3YL1        2   \n",
       "rs7605824_SH3YL1_RPS13  rs7605824_RPS13;SH3YL1  RPS13;SH3YL1        2   \n",
       "rs7605824_SH3YL1_RPL31  rs7605824_RPL31;SH3YL1  RPL31;SH3YL1        2   \n",
       "rs7605824_SH3YL1_RPL3    rs7605824_RPL3;SH3YL1   RPL3;SH3YL1        2   \n",
       "...                                        ...           ...      ...   \n",
       "rs4147638_SMDT1_ACTB      rs4147638_ACTB;SMDT1    ACTB;SMDT1       22   \n",
       "rs4147638_SMDT1_RPS25    rs4147638_RPS25;SMDT1   RPS25;SMDT1       22   \n",
       "rs4147638_SMDT1_RPS3A    rs4147638_RPS3A;SMDT1   RPS3A;SMDT1       22   \n",
       "rs4147638_SMDT1_RPS18    rs4147638_RPS18;SMDT1   RPS18;SMDT1       22   \n",
       "rs4147638_SMDT1_RPL11    rs4147638_RPL11;SMDT1   RPL11;SMDT1       22   \n",
       "\n",
       "                         GenePos  GeneStrand    GeneSymbol        SNP  SNPChr  \\\n",
       "snp_gene1_gene2                                                                 \n",
       "rs7605824_SH3YL1_NPM1     217730         NaN   NPM1;SH3YL1  rs7605824       2   \n",
       "rs7605824_SH3YL1_CD48     217730         NaN   CD48;SH3YL1  rs7605824       2   \n",
       "rs7605824_SH3YL1_RPS13    217730         NaN  RPS13;SH3YL1  rs7605824       2   \n",
       "rs7605824_SH3YL1_RPL31    217730         NaN  RPL31;SH3YL1  rs7605824       2   \n",
       "rs7605824_SH3YL1_RPL3     217730         NaN   RPL3;SH3YL1  rs7605824       2   \n",
       "...                          ...         ...           ...        ...     ...   \n",
       "rs4147638_SMDT1_ACTB    42475695         NaN    ACTB;SMDT1  rs4147638      22   \n",
       "rs4147638_SMDT1_RPS25   42475695         NaN   RPS25;SMDT1  rs4147638      22   \n",
       "rs4147638_SMDT1_RPS3A   42475695         NaN   RPS3A;SMDT1  rs4147638      22   \n",
       "rs4147638_SMDT1_RPS18   42475695         NaN   RPS18;SMDT1  rs4147638      22   \n",
       "rs4147638_SMDT1_RPL11   42475695         NaN   RPL11;SMDT1  rs4147638      22   \n",
       "\n",
       "                          SNPPos SNPAlleles  ... gene1_bios  gene2_bios  \\\n",
       "snp_gene1_gene2                              ...                          \n",
       "rs7605824_SH3YL1_NPM1     280819        G/A  ...     SH3YL1        NPM1   \n",
       "rs7605824_SH3YL1_CD48     280819        G/A  ...     SH3YL1        CD48   \n",
       "rs7605824_SH3YL1_RPS13    280819        G/A  ...     SH3YL1       RPS13   \n",
       "rs7605824_SH3YL1_RPL31    280819        G/A  ...     SH3YL1       RPL31   \n",
       "rs7605824_SH3YL1_RPL3     280819        G/A  ...     SH3YL1        RPL3   \n",
       "...                          ...        ...  ...        ...         ...   \n",
       "rs4147638_SMDT1_ACTB    42487900        G/A  ...      SMDT1        ACTB   \n",
       "rs4147638_SMDT1_RPS25   42487900        G/A  ...      SMDT1       RPS25   \n",
       "rs4147638_SMDT1_RPS3A   42487900        G/A  ...      SMDT1       RPS3A   \n",
       "rs4147638_SMDT1_RPS18   42487900        G/A  ...      SMDT1       RPS18   \n",
       "rs4147638_SMDT1_RPL11   42487900        G/A  ...      SMDT1       RPL11   \n",
       "\n",
       "                        assessed_allele_bios  num_individuals_bios  \\\n",
       "snp_gene1_gene2                                                      \n",
       "rs7605824_SH3YL1_NPM1                      A                2491.0   \n",
       "rs7605824_SH3YL1_CD48                      A                2491.0   \n",
       "rs7605824_SH3YL1_RPS13                     A                2491.0   \n",
       "rs7605824_SH3YL1_RPL31                     A                2491.0   \n",
       "rs7605824_SH3YL1_RPL3                      A                2491.0   \n",
       "...                                      ...                   ...   \n",
       "rs4147638_SMDT1_ACTB                       G                2491.0   \n",
       "rs4147638_SMDT1_RPS25                      G                2491.0   \n",
       "rs4147638_SMDT1_RPS3A                      G                2491.0   \n",
       "rs4147638_SMDT1_RPS18                      G                2491.0   \n",
       "rs4147638_SMDT1_RPL11                      G                2491.0   \n",
       "\n",
       "                        isinteractionterm_bios       snp_genepair_bios  \\\n",
       "snp_gene1_gene2                                                          \n",
       "rs7605824_SH3YL1_NPM1                     True   rs7605824_NPM1;SH3YL1   \n",
       "rs7605824_SH3YL1_CD48                     True   rs7605824_CD48;SH3YL1   \n",
       "rs7605824_SH3YL1_RPS13                    True  rs7605824_RPS13;SH3YL1   \n",
       "rs7605824_SH3YL1_RPL31                    True  rs7605824_RPL31;SH3YL1   \n",
       "rs7605824_SH3YL1_RPL3                     True   rs7605824_RPL3;SH3YL1   \n",
       "...                                        ...                     ...   \n",
       "rs4147638_SMDT1_ACTB                      True    rs4147638_ACTB;SMDT1   \n",
       "rs4147638_SMDT1_RPS25                     True   rs4147638_RPS25;SMDT1   \n",
       "rs4147638_SMDT1_RPS3A                     True   rs4147638_RPS3A;SMDT1   \n",
       "rs4147638_SMDT1_RPS18                     True   rs4147638_RPS18;SMDT1   \n",
       "rs4147638_SMDT1_RPL11                     True   rs4147638_RPL11;SMDT1   \n",
       "\n",
       "                        corrected_p_bios  zscore_bios flipped_zscore_bios  \\\n",
       "snp_gene1_gene2                                                             \n",
       "rs7605824_SH3YL1_NPM1           0.000000    -3.617874           -3.617874   \n",
       "rs7605824_SH3YL1_CD48           0.784422    -0.446946           -0.446946   \n",
       "rs7605824_SH3YL1_RPS13          0.000000    -3.489377           -3.489377   \n",
       "rs7605824_SH3YL1_RPL31          0.349601    -1.325633           -1.325633   \n",
       "rs7605824_SH3YL1_RPL3           0.000000    -3.854851           -3.854851   \n",
       "...                                  ...          ...                 ...   \n",
       "rs4147638_SMDT1_ACTB            0.000000    -3.748326            3.748326   \n",
       "rs4147638_SMDT1_RPS25           0.000000     5.773036           -5.773036   \n",
       "rs4147638_SMDT1_RPS3A           0.000000     4.434777           -4.434777   \n",
       "rs4147638_SMDT1_RPS18           0.000000     7.128733           -7.128733   \n",
       "rs4147638_SMDT1_RPL11           0.000000     5.896748           -5.896748   \n",
       "\n",
       "                       is_concordant  \n",
       "snp_gene1_gene2                       \n",
       "rs7605824_SH3YL1_NPM1           True  \n",
       "rs7605824_SH3YL1_CD48           True  \n",
       "rs7605824_SH3YL1_RPS13          True  \n",
       "rs7605824_SH3YL1_RPL31          True  \n",
       "rs7605824_SH3YL1_RPL3           True  \n",
       "...                              ...  \n",
       "rs4147638_SMDT1_ACTB            True  \n",
       "rs4147638_SMDT1_RPS25           True  \n",
       "rs4147638_SMDT1_RPS3A           True  \n",
       "rs4147638_SMDT1_RPS18           True  \n",
       "rs4147638_SMDT1_RPL11           True  \n",
       "\n",
       "[497 rows x 55 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeqtl_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flip direction according to AF\n",
    "coeqtl_df['eqtl_effect_allele'] = [eqtl_allele_af_dict.get(eqtl)['AlleleAssessed'] for eqtl in \n",
    "                                       coeqtl_df['snp_eqtlgene']]\n",
    "coeqtl_df['eqtl_alt_af'] = [eqtl_allele_af_dict.get(eqtl)['AF'] for eqtl in coeqtl_df['snp_eqtlgene']]\n",
    "coeqtl_df['eqtl_alt_allele'] = [eqtl_allele_af_dict.get(eqtl)['alt_allele'] for eqtl in \n",
    "                                    coeqtl_df['snp_eqtlgene']]\n",
    "coeqtl_df['eqtl_ref_allele'] = [eqtl_allele_af_dict.get(eqtl)['ref_allele'] for eqtl in \n",
    "                                    coeqtl_df['snp_eqtlgene']]\n",
    "coeqtl_df[f'MetaPZ_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
    "                                                   for zscore, coeqtlallele, altaf, altallele in\n",
    "                                                   coeqtl_df[[f'MetaPZ',\n",
    "                                                                  f'SNPEffectAllele',\n",
    "                                                                  'eqtl_alt_af',\n",
    "                                                                  'eqtl_alt_allele']].values]\n",
    "coeqtl_df[f'flipped_zscore_bios_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
    "                                                   for zscore, coeqtlallele, altaf, altallele in\n",
    "                                                   coeqtl_df[[f'flipped_zscore_bios',\n",
    "                                                                  f'SNPEffectAllele',\n",
    "                                                                  'eqtl_alt_af',\n",
    "                                                                  'eqtl_alt_allele']].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9637681159420289\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(3, -5, 'Concordance = 0.96\\nrb = 0.61')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
    "coeqtl_nonsig = coeqtl_df[coeqtl_df['corrected_p_bios']>0.05]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.scatter(coeqtl_nonsig['MetaPZ_flippedforAF'], \n",
    "            coeqtl_nonsig['flipped_zscore_bios_flippedforAF'], \n",
    "            label='Insignificant',\n",
    "            edgecolor='gray',\n",
    "            facecolor='white', alpha=1)\n",
    "plt.scatter(coeqtl_sig['MetaPZ_flippedforAF'],\n",
    "            coeqtl_sig['flipped_zscore_bios_flippedforAF'],  \n",
    "            label='Significant',\n",
    "            edgecolor=color_dict[celltype],\n",
    "            facecolor=color_dict[celltype], alpha=1)\n",
    "plt.plot([-15, 12], [0, 0], linestyle='--', color='lightgray')\n",
    "plt.plot([0, 0], [-6.5, 4], linestyle='--', color='lightgray')\n",
    "plt.legend()\n",
    "\n",
    "concordance_rate = coeqtl_sig[coeqtl_sig['is_concordant']].shape[0] / coeqtl_sig.shape[0]\n",
    "print(concordance_rate)\n",
    "\n",
    "celltype_rb = bios_replication_filtered_df.loc[celltype]['r']\n",
    "plt.text(3, -5, f'Concordance = {concordance_rate:.2f}\\nrb = {celltype_rb:.2f}')\n",
    "\n",
    "# plt.savefig('bios_replication.cd4t.filtered_results.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None):\n",
    "    \"\"\"Return an axes of confidence bands using a simple approach.\n",
    "    \n",
    "    Notes\n",
    "    -----\n",
    "    .. math:: \\left| \\: \\hat{\\mu}_{y|x0} - \\mu_{y|x0} \\: \\right| \\; \\leq \\; T_{n-2}^{.975} \\; \\hat{\\sigma} \\; \\sqrt{\\frac{1}{n}+\\frac{(x_0-\\bar{x})^2}{\\sum_{i=1}^n{(x_i-\\bar{x})^2}}}\n",
    "    .. math:: \\hat{\\sigma} = \\sqrt{\\sum_{i=1}^n{\\frac{(y_i-\\hat{y})^2}{n-2}}}\n",
    "    \n",
    "    References\n",
    "    ----------\n",
    "    .. [1] M. Duarte.  \"Curve fitting,\" Jupyter Notebook.\n",
    "       http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/CurveFitting.ipynb\n",
    "    \n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    \n",
    "    ci = t * s_err * np.sqrt(1/n + (x2 - np.mean(x))**2 / np.sum((x - np.mean(x))**2))\n",
    "    ax.fill_between(x2, y2 + ci, y2 - ci, alpha=0.1, color='gray')\n",
    "    return ax\n",
    "\n",
    "from scipy import stats\n",
    "def equation(a, b):\n",
    "    \"\"\"Return a 1D polynomial.\"\"\"\n",
    "    return np.polyval(a, b) \n",
    "\n",
    "x=coeqtl_df['MetaPZ_flippedforAF']\n",
    "y=coeqtl_df['flipped_zscore_bios_flippedforAF']\n",
    "\n",
    "p, cov = np.polyfit(x, y, 1, cov=True)                     # parameters and covariance from of the fit of 1-D polynom.\n",
    "y_model = equation(p, x)     \n",
    "# Statistics\n",
    "n = y.size                                           # number of observations\n",
    "m = p.size                                                 # number of parameters\n",
    "dof = n - m                                                # degrees of freedom\n",
    "t = stats.t.ppf(0.975, n - m)                              # used for CI and PI bands\n",
    "# Estimates of Error in Data/Model\n",
    "resid = y - y_model                           \n",
    "chi2 = np.sum((resid / y_model)**2)                        # chi-squared; estimates error in data\n",
    "chi2_red = chi2 / dof                                      # reduced chi-squared; measures goodness of fit\n",
    "s_err = np.sqrt(np.sum(resid**2) / dof)                    # standard deviation of the error\n",
    "\n",
    "# Plotting --------------------------------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "# Data\n",
    "ax.scatter(\n",
    "    x, y\n",
    ")\n",
    "\n",
    "\n",
    "# Fit\n",
    "ax.plot(x, y_model, \"-\", color=\"0.1\", linewidth=1.5, alpha=0.5, label=\"Fit\")  \n",
    "\n",
    "x2 = np.linspace(np.min(x), np.max(x), 100)\n",
    "y2 = equation(p, x2)\n",
    "\n",
    "# Confidence Interval (select one)\n",
    "plot_ci_manual(t, s_err, n, x, x2, y2, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-46-55674587bbd9>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  coeqtl_sig['celltype'] = celltype\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6de56225b0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# withbiostechnicalandcelltypePICs\n",
    "sig_df = pd.DataFrame()\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)\n",
    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        celltype = celltypes[i*3+j]\n",
    "        coeqtl_df = pd.read_csv(\n",
    "            coeqtl_withbios_prefix/filter_type/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbiosonlyRNAAlignMetrics_rmLLD.tsv.gz',\n",
    "            compression='gzip', index_col=0, sep='\\t')\n",
    "        coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
    "                                    coeqtl_df[['t_bios', \n",
    "                                               'num_individuals_bios']].values]\n",
    "        coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
    "                                          coeqtl_df[['SNPEffectAllele', \n",
    "                                                     'assessed_allele_bios',\n",
    "                                                     'zscore_bios']].values]\n",
    "        # flip the direction according to AF\n",
    "        coeqtl_df['eqtl_effect_allele'] = [eqtl_allele_af_dict.get(eqtl)['AlleleAssessed'] for eqtl in \n",
    "                                       coeqtl_df['snp_eqtlgene']]\n",
    "        coeqtl_df['eqtl_alt_af'] = [eqtl_allele_af_dict.get(eqtl)['AF'] for eqtl in coeqtl_df['snp_eqtlgene']]\n",
    "        coeqtl_df['eqtl_alt_allele'] = [eqtl_allele_af_dict.get(eqtl)['alt_allele'] for eqtl in \n",
    "                                            coeqtl_df['snp_eqtlgene']]\n",
    "        coeqtl_df['eqtl_ref_allele'] = [eqtl_allele_af_dict.get(eqtl)['ref_allele'] for eqtl in \n",
    "                                            coeqtl_df['snp_eqtlgene']]\n",
    "        coeqtl_df[f'MetaPZ_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
    "                                                           for zscore, coeqtlallele, altaf, altallele in\n",
    "                                                           coeqtl_df[[f'MetaPZ',\n",
    "                                                                          f'SNPEffectAllele',\n",
    "                                                                          'eqtl_alt_af',\n",
    "                                                                          'eqtl_alt_allele']].values]\n",
    "        coeqtl_df[f'flipped_zscore_bios_flippedforAF'] = [flip_zscore(zscore, coeqtlallele, altaf, altallele)\n",
    "                                                           for zscore, coeqtlallele, altaf, altallele in\n",
    "                                                           coeqtl_df[[f'flipped_zscore_bios',\n",
    "                                                                          f'SNPEffectAllele',\n",
    "                                                                          'eqtl_alt_af',\n",
    "                                                                          'eqtl_alt_allele']].values]\n",
    "        ## end flip\n",
    "        coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
    "        coeqtl_sig['celltype'] = celltype\n",
    "        sig_df = pd.concat([coeqtl_sig, sig_df], axis=0)\n",
    "        significant_ratio = coeqtl_sig.shape[0] / coeqtl_df.shape[0]\n",
    "        coeqtl_sig_samedirection = coeqtl_sig[((coeqtl_sig['MetaPZ']>0) & (coeqtl_sig['flipped_zscore_bios']>0)) | \n",
    "                                              ((coeqtl_sig['MetaPZ']<0) & (coeqtl_sig['flipped_zscore_bios']<0))]\n",
    "        consistent_ratio = coeqtl_sig_samedirection.shape[0] / coeqtl_sig.shape[0]\n",
    "        # draw\n",
    "        ax = axes[i][j]\n",
    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']>0.05], \n",
    "                   coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']>0.05], alpha=0.5,\n",
    "                   label='Non-sig')\n",
    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']<=0.05],\n",
    "                    coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']<=0.05], alpha=0.5,\n",
    "                    label='Sig')\n",
    "        ax.set_xlabel('single cell')\n",
    "        ax.set_ylabel('BIOS')\n",
    "        ax.set_title(celltype)\n",
    "        ax.text(-2, -8, \n",
    "                f'Significant ratio: {significant_ratio:.2f}\\nConcordance ratio: {consistent_ratio:.2f}')\n",
    "ax.legend(loc='upper left')\n",
    "        \n",
    "# plt.savefig('bios_replication.filtered_results.scatterplots.pdf')\n",
    "# plt.savefig('bios_replication.filtered_results.scatterplots.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-0693f2cb76ec>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  coeqtl_sig['celltype'] = celltype\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6dedb25820>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# unfiltered results \n",
    "# withbiosonlyRNAAlignMetrics_rmLLD\n",
    "sig_df = pd.DataFrame()\n",
    "fig, axes = plt.subplots(2, 3, figsize=(15, 10), sharex=True, sharey=True)\n",
    "celltypes = ['CD4T', 'CD8T', 'monocyte', 'NK', 'B', 'DC']\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        celltype = celltypes[i*3+j]\n",
    "        coeqtl_df = pd.read_csv(\n",
    "            coeqtl_withbios_prefix/'unfiltered_results'/f'UT_{celltype}/coeqtls_fullresults_fixed.sig.withbiosonlyRNAAlignMetrics_rmLLD.tsv.gz',\n",
    "            compression='gzip', index_col=0, sep='\\t')\n",
    "        coeqtl_df['zscore_bios'] = [get_z_score(item[0], item[1]) for item in \n",
    "                                    coeqtl_df[['t_bios', \n",
    "                                               'num_individuals_bios']].values]\n",
    "        coeqtl_df['flipped_zscore_bios'] = [flip_direction(item[0], item[1], item[2]) for item in \n",
    "                                          coeqtl_df[['SNPEffectAllele', \n",
    "                                                     'assessed_allele_bios',\n",
    "                                                     'zscore_bios']].values]\n",
    "        coeqtl_sig = coeqtl_df[coeqtl_df['corrected_p_bios']<=0.05]\n",
    "        coeqtl_sig['celltype'] = celltype\n",
    "        sig_df = pd.concat([coeqtl_sig, sig_df], axis=0)\n",
    "        # draw\n",
    "        ax = axes[i][j]\n",
    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']>0.05], \n",
    "                   coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']>0.05], alpha=0.5,\n",
    "                   label='Non-sig')\n",
    "        ax.scatter(coeqtl_df['MetaPZ'][coeqtl_df['corrected_p_bios']<=0.05],\n",
    "                    coeqtl_df['flipped_zscore_bios'][coeqtl_df['corrected_p_bios']<=0.05], alpha=0.5,\n",
    "                    label='Sig')\n",
    "        ax.set_xlabel('single cell')\n",
    "        ax.set_ylabel('BIOS')\n",
    "        ax.set_title(celltype)\n",
    "ax.legend(loc='upper left')\n",
    "# plt.savefig('bios_replication.unfiltered_results.scatterplots.pdf')\n",
    "# plt.savefig('bios_replication.unfiltered_results.scatterplots.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
